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<center><b>
 [ <a href="module-tree.html">Module Hierarchy</a>
 | <a href="class-tree.html">Class Hierarchy</a> ]
</b></center><br />
<h1 class="epydoc">Class Hierarchy</h1>
<ul class="nomargin-top">
    <li> <strong class="uidlink">peach.optm.Optimizer</strong>
    </li>
    <li> <strong class="uidlink">object</strong>:
      <em class="summary">The most base type</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.pso.acc.Accelerator-class.html">peach.pso.acc.Accelerator</a></strong>:
      <em class="summary">Base class for accelerators.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.pso.acc.StandardPSO-class.html">peach.pso.acc.StandardPSO</a></strong>:
      <em class="summary">Standard PSO Accelerator</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Activation-class.html">peach.nn.af.Activation</a></strong>:
      <em class="summary">Base class for activation functions.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.af.ArcTan-class.html">peach.nn.af.ArcTan</a></strong>:
      <em class="summary">Inverse tangent activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Linear-class.html">peach.nn.af.Linear</a></strong>:
      <em class="summary">Identity activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.RadialBasis-class.html">peach.nn.af.RadialBasis</a></strong>:
      <em class="summary">This class is used as a base class for radial basis functions (RBFs). It is
in almost every aspect equal to <tt class="rst-docutils literal">Activation</tt> class, but it is used to
distinguish the two types. RBFs are used in Radial Basis Function Networks,
in which monotonic activations shouldn't be used.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.af.Gaussian-class.html">peach.nn.af.Gaussian</a></strong>:
      <em class="summary">Gaussian activation function</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Ramp-class.html">peach.nn.af.Ramp</a></strong>:
      <em class="summary">Ramp activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Sigmoid-class.html">peach.nn.af.Sigmoid</a></strong>:
      <em class="summary">Sigmoid activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Signum-class.html">peach.nn.af.Signum</a></strong>:
      <em class="summary">Signum activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.TanH-class.html">peach.nn.af.TanH</a></strong>:
      <em class="summary">Hyperbolic tangent activation function</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.af.Threshold-class.html">peach.nn.af.Threshold</a></strong>:
      <em class="summary">Threshold activation function.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor.BinaryNeighbor-class.html">peach.sa.neighbor.BinaryNeighbor</a></strong>:
      <em class="summary">Base class for binary neighbor functions</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor.InvertBitsNeighbor-class.html">peach.sa.neighbor.InvertBitsNeighbor</a></strong>:
      <em class="summary">A simple neighborhood based on the change of a few bits.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa.base.BinarySA-class.html">peach.sa.base.BinarySA</a></strong>:
      <em class="summary">Simulated Annealing binary optimization.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor.ContinuousNeighbor-class.html">peach.sa.neighbor.ContinuousNeighbor</a></strong>:
      <em class="summary">Base class for continuous neighbor functions</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor.GaussianNeighbor-class.html">peach.sa.neighbor.GaussianNeighbor</a></strong>:
      <em class="summary">A new estimate based on a gaussian distribution</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa.neighbor.UniformNeighbor-class.html">peach.sa.neighbor.UniformNeighbor</a></strong>:
      <em class="summary">A new estimate based on a uniform distribution</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.sa.base.ContinuousSA-class.html">peach.sa.base.ContinuousSA</a></strong>:
      <em class="summary">Simulated Annealing continuous optimization.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.control.Controller-class.html">peach.fuzzy.control.Controller</a></strong>:
      <em class="summary">Basic Mamdani controller</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.fuzzy.control.Mamdani-class.html">peach.fuzzy.control.Mamdani</a></strong>:
      <em class="summary"><tt class="rst-docutils literal">Mandani</tt> is an alias to <tt class="rst-docutils literal">Controller</tt></em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.crossover.Crossover-class.html">peach.ga.crossover.Crossover</a></strong>:
      <em class="summary">Base class for crossover operators.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.crossover.OnePoint-class.html">peach.ga.crossover.OnePoint</a></strong>:
      <em class="summary">A one-point crossover operator.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.crossover.TwoPoint-class.html">peach.ga.crossover.TwoPoint</a></strong>:
      <em class="summary">A two-point crossover operator.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.crossover.Uniform-class.html">peach.ga.crossover.Uniform</a></strong>:
      <em class="summary">A uniform crossover operator.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.FFLearning-class.html">peach.nn.lrules.FFLearning</a></strong>:
      <em class="summary">Base class for FeedForwarding Multilayer neural networks.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.BackPropagation-class.html">peach.nn.lrules.BackPropagation</a></strong>:
      <em class="summary">The BackPropagation learning method.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.LMS-class.html">peach.nn.lrules.LMS</a></strong>:
      <em class="summary">The Least-Mean-Square (LMS) learning method.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.fitness.Fitness-class.html">peach.ga.fitness.Fitness</a></strong>:
      <em class="summary">Base class for fitness function classifiers.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.fitness.Ranking-class.html">peach.ga.fitness.Ranking</a></strong>:
      <em class="summary">Ranking fitness for a population</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.cmeans.FuzzyCMeans-class.html">peach.fuzzy.cmeans.FuzzyCMeans</a></strong>:
      <em class="summary">Fuzzy C-Means convergence.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.nnet.GRNN-class.html">peach.nn.nnet.GRNN</a></strong>:
      <em class="summary">GRNN is the implementation of General Regression Neural Network, a kind of
probabilistic neural network used in regression tasks.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.kmeans.KMeans-class.html">peach.nn.kmeans.KMeans</a></strong>:
      <em class="summary">K-Means clustering algorithm</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.base.Layer-class.html">peach.nn.base.Layer</a></strong>:
      <em class="summary">Base class for neural networks.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.mem.Hopfield-class.html">peach.nn.mem.Hopfield</a></strong>:
      <em class="summary">Hopfield neural network model</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.nnet.SOM-class.html">peach.nn.nnet.SOM</a></strong>:
      <em class="summary">A Self-Organizing Map (SOM).</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Membership-class.html">peach.fuzzy.mf.Membership</a></strong>:
      <em class="summary">Base class of all membership functions.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Bell-class.html">peach.fuzzy.mf.Bell</a></strong>:
      <em class="summary">Generalized Bell function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.DecreasingRamp-class.html">peach.fuzzy.mf.DecreasingRamp</a></strong>:
      <em class="summary">Decreasing ramp.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.DecreasingSigmoid-class.html">peach.fuzzy.mf.DecreasingSigmoid</a></strong>:
      <em class="summary">Decreasing Sigmoid function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Gaussian-class.html">peach.fuzzy.mf.Gaussian</a></strong>:
      <em class="summary">Gaussian function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.IncreasingRamp-class.html">peach.fuzzy.mf.IncreasingRamp</a></strong>:
      <em class="summary">Increasing ramp.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.IncreasingSigmoid-class.html">peach.fuzzy.mf.IncreasingSigmoid</a></strong>:
      <em class="summary">Increasing Sigmoid function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.RaisedCosine-class.html">peach.fuzzy.mf.RaisedCosine</a></strong>:
      <em class="summary">Raised Cosine function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Smf-class.html">peach.fuzzy.mf.Smf</a></strong>:
      <em class="summary">Increasing smooth curve with 0 and 1 minimum and maximum values outside a
given range.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Trapezoid-class.html">peach.fuzzy.mf.Trapezoid</a></strong>:
      <em class="summary">Trapezoid function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Triangle-class.html">peach.fuzzy.mf.Triangle</a></strong>:
      <em class="summary">Triangle function.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.mf.Zmf-class.html">peach.fuzzy.mf.Zmf</a></strong>:
      <em class="summary">Decreasing smooth curve with 0 and 1 minimum and maximum values outside a
given range.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.mutation.Mutation-class.html">peach.ga.mutation.Mutation</a></strong>:
      <em class="summary">Base class for mutation operators.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.mutation.BitToBit-class.html">peach.ga.mutation.BitToBit</a></strong>:
      <em class="summary">A simple bit-to-bit mutation operator.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.base.Optimizer-class.html">peach.optm.base.Optimizer</a></strong>:
      <em class="summary">Base class for all optimizers.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.optm.quasinewton.BFGS-class.html">peach.optm.quasinewton.BFGS</a></strong>:
      <em class="summary">BFGS (<em>Broyden-Fletcher-Goldfarb-Shanno</em>) search</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.quasinewton.DFP-class.html">peach.optm.quasinewton.DFP</a></strong>:
      <em class="summary">DFP (<em>Davidon-Fletcher-Powell</em>) search</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.multivar.Direct-class.html">peach.optm.multivar.Direct</a></strong>:
      <em class="summary">Multidimensional direct search</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.linear.Direct1D-class.html">peach.optm.linear.Direct1D</a></strong>:
      <em class="summary">1-D direct search.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.linear.Fibonacci-class.html">peach.optm.linear.Fibonacci</a></strong>:
      <em class="summary">Optimization by the Golden Rule Section, estimated by Fibonacci numbers.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.linear.GoldenRule-class.html">peach.optm.linear.GoldenRule</a></strong>:
      <em class="summary">Optimizer by the Golden Section Rule</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.multivar.Gradient-class.html">peach.optm.multivar.Gradient</a></strong>:
      <em class="summary">Gradient search</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.linear.Interpolation-class.html">peach.optm.linear.Interpolation</a></strong>:
      <em class="summary">Optimization by quadractic interpolation.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.multivar.MomentumGradient-class.html">peach.optm.multivar.MomentumGradient</a></strong>:
      <em class="summary">Gradient search with momentum</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.multivar.Newton-class.html">peach.optm.multivar.Newton</a></strong>:
      <em class="summary">Newton search</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.optm.quasinewton.SR1-class.html">peach.optm.quasinewton.SR1</a></strong>:
      <em class="summary">SR1 (<em>Symmetric Rank 1</em> ) search method</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.nnet.PNN-class.html">peach.nn.nnet.PNN</a></strong>:
      <em class="summary">PNN is the implementation of Probabilistic Neural Network, a network used
for classification tasks</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.fuzzy.control.Parametric-class.html">peach.fuzzy.control.Parametric</a></strong>:
      <em class="summary">Basic Parametric controller</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.fuzzy.control.Sugeno-class.html">peach.fuzzy.control.Sugeno</a></strong>:
      <em class="summary"><tt class="rst-docutils literal">Sugeno</tt> is an alias to <tt class="rst-docutils literal">Parametric</tt></em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.rbfn.RBFN-class.html">peach.nn.rbfn.RBFN</a></strong>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.SOMLearning-class.html">peach.nn.lrules.SOMLearning</a></strong>:
      <em class="summary">Base class for Self-Organizing Maps.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.Competitive-class.html">peach.nn.lrules.Competitive</a></strong>:
      <em class="summary">Competitive learning with time adjust of the learning rate.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.Cooperative-class.html">peach.nn.lrules.Cooperative</a></strong>:
      <em class="summary">Cooperative learning with time adjust of the learning rate and neighborhood
function to propagate cooperation</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.nn.lrules.WinnerTakesAll-class.html">peach.nn.lrules.WinnerTakesAll</a></strong>:
      <em class="summary">Purely competitive learning method without learning rate adjust.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.selection.Selection-class.html">peach.ga.selection.Selection</a></strong>:
      <em class="summary">Base class for selection operators.</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.selection.Baker-class.html">peach.ga.selection.Baker</a></strong>:
      <em class="summary">The Baker selection method.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.selection.BinaryTournament-class.html">peach.ga.selection.BinaryTournament</a></strong>:
      <em class="summary">The Binary Tournament selection method.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.selection.RouletteWheel-class.html">peach.ga.selection.RouletteWheel</a></strong>:
      <em class="summary">The Roulette Wheel selection method.</em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink">bitarray._bitarray</strong>
    <ul>
    <li> <strong class="uidlink">bitarray.bitarray</strong>:
      <em class="summary">bitarray([initial][endian=string])</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.chromosome.Chromosome-class.html">peach.ga.chromosome.Chromosome</a></strong>:
      <em class="summary">Implements a chromosome as a bit array.</em>
    </li>
    </ul>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink">list</strong>:
      <em class="summary">list() -&gt; new empty list
list(iterable) -&gt; new list initialized from iterable's items</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.nn.nnet.FeedForward-class.html">peach.nn.nnet.FeedForward</a></strong>:
      <em class="summary">Classic completely connected neural network.</em>
    </li>
    <li> <strong class="uidlink"><a href="peach.ga.base.GeneticAlgorithm-class.html">peach.ga.base.GeneticAlgorithm</a></strong>:
      <em class="summary">A standard Genetic Algorithm</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.ga.base.GA-class.html">peach.ga.base.GA</a></strong>:
      <em class="summary">GA is an alias to <tt class="rst-docutils literal">GeneticAlgorithm</tt></em>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink"><a href="peach.pso.base.ParticleSwarmOptimizer-class.html">peach.pso.base.ParticleSwarmOptimizer</a></strong>:
      <em class="summary">A standard Particle Swarm Optimizer</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.pso.base.PSO-class.html">peach.pso.base.PSO</a></strong>:
      <em class="summary">PSO is an alias to <tt class="rst-docutils literal">ParticleSwarmOptimizer</tt></em>
    </li>
    </ul>
    </li>
    </ul>
    </li>
    <li> <strong class="uidlink">numpy.ndarray</strong>:
      <em class="summary">strides=None, order=None)</em>
    <ul>
    <li> <strong class="uidlink"><a href="peach.fuzzy.base.FuzzySet-class.html">peach.fuzzy.base.FuzzySet</a></strong>:
      <em class="summary">Array containing fuzzy values for a set.</em>
    </li>
    </ul>
    </li>
    </ul>
    </li>
</ul>
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